Clustering of multivariate time series data using particle swarm optimization

2015 ◽  
Author(s):  
Abbas Ahmadi ◽  
Atefeh Mozafarinia ◽  
Azadeh Mohebi
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Particle Swarm ◽  
Series Data ◽  
Swarm Optimization

Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network

2016 ◽  
Vol 7 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Rajashree Dash ◽  
Pradipta Kishore Dash
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Time Series Data ◽  
Financial Time Series ◽  
Random Search ◽  
Particle Swarm ◽  
Series Data ◽  
Swarm Optimization ◽  
Financial Time

In this paper a predictor model using Legendre Neural Network is proposed for one day ahead prediction of financial time series data. The Legendre Neural Network (LENN) is a single layer structure that possess faster convergence rate and reduced computational complexity by increasing the dimensionality of the input pattern with a set of linearly independent nonlinear functions. The parameters of the LENN model are estimated using a Moderate Random Search Particle Swarm Optimization Method (HMRPSO). The HMRPSO is a variant of PSO that uses a moderate random search method to enhance the global search ability of particles and increases their convergence rates by focusing on valuable search space regions. Training LENN using HMRPSO has also been compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of LENN for predicting the Bombay Stock Exchange and S&P 500 data sets.

Developing intuitionistic fuzzy seasonality regression with particle swarm optimization for air pollution forecasting

2019 ◽  
Vol 119 (3) ◽  
pp. 561-577 ◽  
Author(s):  
Chung-Han Ho ◽  
Ping-Teng Chang ◽  
Kuo-Chen Hung ◽  
Kuo-Ping Lin
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Time Series Data ◽  
Series Data ◽  
Swarm Optimization ◽  
Content Type ◽  
Intuitionistic Fuzzy ◽  
Seasonal Time Series ◽  
Air Pollutions ◽  
Daily Concentration

PurposeThe purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).Design/methodology/approachThe prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.FindingsSeasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.Originality/valueThis study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.

scholarly journals MODEL FEED FORWARD NEURAL NETWORK (FFNN) DENGAN ALGORITMA PARTICLE SWARM SEBAGAI OPTIMASI BOBOT (Studi Kasus : Harga Daging Sapi dari Bank Dunia Periode Januari 2007 – Desember 2018)

2019 ◽  
Vol 8 (1) ◽  
pp. 117-126
Author(s):  
Faisal Fikri Utama ◽  
Budi Warsito ◽  
Sugito Sugito
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Time Series Data ◽  
Parametric Method ◽  
Particle Swarm ◽  
Series Data ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Particle Speed

Beef is one of the important food commodities to fulfill the nutritional adequacy of humans. The World Bank notes the beef prices that are exported worldwide every month. For this reason, those data becomes a predictable series for the next period. Feed Forward Neural Network is a non-parametric method that can be used to make predictions from time series data without having to be bound by classical assumptions. The initiated weight will be evaluated by an algorithm that can minimize errors. Particle Swarm Optimization (PSO) is an optimization algorithm based on particle speed to reach the destination. The FFNN model will be combined with PSO to get predictive results that are close to the target. The best architecture on FFNN is obtained with 2 units of input, 1 unit of bias, 3 hidden units, and 1 unit of output by producing MAPE training 11.7735% and MAPE testing 8.14%. According to Lewis (1982) in Moreno et. al (2013), the MAPE value below 10% is highly accurate forecasting. Keywords: Feed Forward Neural Network (FFNN), Particle Swarm Optimization (PSO), neurons, weights, predictions.

scholarly journals PARTICLE SWARM OPTIMIZATION SEBAGAI PENENTU NILAI BOBOT PADA ARTIFICIAL NEURAL NETWORK BERBASIS BACKPROPAGATION UNTUK PREDIKSI TINGKAT PENJUALAN MINYAK PELUMAS PERTAMINA

2018 ◽  
Vol 3 (1) ◽  
pp. 183-192
Author(s):  
Muhammad Ali Ridla
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Time Series Data ◽  
Linear Time ◽  
Particle Swarm ◽  
Lubricating Oil ◽  
Series Data ◽  
Swarm Optimization ◽  
Artificial Neural

The lubricating oil industry is one part of the oil and gas sector which is still one of the main pillars of economic growth in Indonesia. Sales predictions are needed by companies and policy makers as planning materials and economic development strategies to increase income in the future. Predictions that have a better level of accuracy can provide appropriate decisions. Various methods have been used, the Artificial Neural Network algorithm is one of the most widely used, especially in the Backpropagation (BPNN) structure which can predict non linear time series data. Backpropagation has been proven to have a better level of accuracy compared to econometric methods such as ARIMA. The integration of Backpropagation algorithm with other algorithms needs to be done to overcome the shortcomings and improve the ability of the National Land Agency itself. Particle Swarm Optimization (PSO) which is used as an optimization determinant of attribute weight values in the network structure of BPNN shows good results. After testing, BPNN without PSO has a Squared Error (SE) level of 0.012 and a Root Mean Aquared Error (RMSE) of 0.111. While BPNN with PSO has SE levels of 0.004 and RMSE of 0.059. This shows that there is a significant decrease in the error rate after the PSO algorithm is added to the BPNN structure which is 46.85%.

Sign in / Sign up

Export Citation Format

Share Document